SUMMARY: Visualization of multidimensional, categorical data is a common challenge across scientific domains and, in particular, the life sciences. The goal is to create a comprehensive overview of the underlying data which enables one to assess multiple variables. One application where such visualizations are particularly useful is gene or pathway analysis, which involves checking for dysregulation in known biological mechanisms and functions across multiple conditions. Here, we propose a new visualization approach that encodes such data in an intuitive representation: DicePlots visualize up to four distinct categorical classes in a single view using elements resembling dice faces, whereas DominoPlots add an additional layer of information for binary comparison. AVAILABILITY AND IMPLEMENTATION: The code is available as the diceplot R package and the pydiceplot on PyPI. All source code is available at https://github.com/maflot. CONTACT: The repo is managed actively and we encourage community contributions and requests.